Deep learning methods have shown promising results in on-road freespace detection with the public on-road datasets [1]}, [2]}. Only the RGB image lacking the geometry information is insufficient for freespace detection. We provide the ORFD dataset with the RGB image, LiDAR point cloud, calibration, sparse depth, dense depth and ground truth. The sparse depth was obtained by projecting the LiDAR point cloud to the image plane with the calibration information and then the sparse depth was interpolated to get the dense depth. We split the dataset into training, validation and testing set as shown in Table REF . From the table, we can see that the ratio of the three sets is about \(7:1:2\) . As it is hard to define road in grassland, we only choose one sequence of well-annotated data in grassland for training set.
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